Quais são as melhores maneiras de comparar o desempenho do sistema em diferentes configurações?

Multiply hardware capability and software capability and compare as a whole with another machine For example, Linux can run super fast in i3 - 7020U having 2GB of RAM and 40GB of mechanical hard drive with integrated graphics of UHD 620 .. but with the same configuration windows 11 cannot boot up at the same time as Linux . So, a budget oriented developer can go with Linux based development rather than using windows11 .. heavyness of the OS plays key role for the swift usage of computer.. that's why Linux is minimising it's size for its users and that's very easy to compress the OS without any loss only in Linux... windows is a mid tier heavy os.. if compared to ParrotSec os and Linux mint

Choosing appropriate metrics is an important step in comparing system performance in different settings. Metrics, which are quantitative indicators of system performance, must be aligned with your specific goals and requirements. Your choice of metrics should reflect the aspects of system performance that are most relevant to your workload, user experience, or resource efficiency goals. For example, when evaluating how different operating systems handle web server requests, metrics such as requests per second, average response time, or error rate are important for a meaningful comparison. Therefore, it is important to carefully select the metrics that best meet your performance evaluation needs.

This is actually the second step. The first step is to identify the objective of the testing. Are you trying to disqualify hardware? Are you trying to find performance maximums? Are you looking for a realistic estimate of expected production performance? Once you define this question, it will help inform which metrics you want to gather and how you want to do so. Different benchmarking approaches are not appropriate for all uses and can often provide misleading results, particularly when resource contention across multiple components is at play.

Comparing system performance across different setups involves selecting the right performance metrics, using benchmarking tools, conducting controlled experiments, and employing monitoring tools. It's essential to consider scalability and use realistic workloads for accurate results. Statistical analysis ensures you identify significant performance differences, and replicating tests helps account for variability. Thorough documentation is key for transparency and future reference, allowing you to make informed decisions to optimize your system's performance.

Based on my experience, my extra recommendation to this excellent article is to start by identifying the purpose of the whole exercise. Is it to increase performance? Or is it about avoiding extra spending? Or boosting the price/performance ratio of the machine? Another important point to consider when identifying metrics is to select parameters that are most likely to impact the user experience. This could be deduced by intuitively applying Amdahl's law: "The overall performance improvement gained by optimizing a single part of a system is limited by the fraction of time that the improved part is actually used". In other terms, the right metrics are those that match the most frequent use case of the machine."

To check system performance with different configurations is 2 steps roll coaster. 1 steps: Discuss briefly with the client about workload, number of users concurrency, storage requirements. Proposed systems as per above requirements. 2. Its hard to calculate perfectly, project requirement, workload, new data models, growth in data etc are the common factors for performance degradation. At that point we have to work on system configuration to boost the performance. For example in db2 It is good practice to set aside some dedicated (unshared) disk for the DB2 transaction logs. This is because the competition between log I/O and other types of I/O can result in a logging bottleneck, especially in systems with a high degree of write activity

Not Just benchmarking , but make sure we do right Comparative and Competitive analysis. We have many aspects which impact performance , system config like TDP , CPU frequency ( Single core / max core turbo ) , Current , Voltage , CPU metric like instruction set ,clock speed , bandwidth , front side but-FSB ( many times this is bottleneck ) and on-board cache . FSB is interface between processor and system memory . Choosing right benchmark / workload and finding which of above metric mentioned will impact and have comparatively analysis accordingly by fine graining , Competitive analysis as name suggest find the competition run same set of workload and analysis performance and try to find which is bottleneck for your system performance

Benchmarks offer a structured means of gauging performance. Using irrelevant or non-representative benchmarks can skew results, giving a misleading view of performance. For example, with a server primarily used for data transactions, using a graphics-intensive benchmark like Cinebench would be unsuitable. Instead, a more appropriate benchmark might be Sysbench's OLTP test, which evaluates database performance. Picking the correct benchmark ensures that the tested performance aligns with real-world use cases.

To compare system performance across configurations, choose appropriate benchmarks that replicate realistic scenarios. Benchmarks are standardized tests that provide comparable metrics. Select benchmarks tailored to your system type, application, and performance goals.

Choosing the right benchmarks is a crucial aspect of evaluating system performance accurately and effectively. The selection process can vary depending on your specific needs and the nature of the system you are assessing. Define Your Goals: Clearly state your performance evaluation objectives. Know Your System: Consider the type of system being assessed. Identify Relevant Scenarios: Focus on workloads or scenarios relevant to your system's usage. Research Benchmarks: Find reputable benchmarking tools suited to your needs. Verify Reliability: Ensure the benchmarks are consistent and reliable. Keep It Updated: Stay current with benchmarking tools as technology advances.

Choosing the right tools is a critical step in comparing system performance in different settings. These tools should be able to efficiently collect, analyze and visualize your chosen metrics and benchmarks. The choice of tools must meet your specific needs, taking into account factors such as system-platform compatibility, benchmark set and analysis methods. For example, when you evaluate the effect of different operating systems on the battery life, you can choose tools such as BatteryMon, PowerTOP or Battery Eater, because they are adapted for this activity. That's why it's important to choose tools that are well-suited to your performance evaluation goals and meet the capabilities of your system.

Selecting the right tools to compare system performance is vital, but their choice should be guided by the specific needs of your analysis. Prioritize selecting relevant metrics and benchmarks first, and then choose tools that fit your use case. Consider using multiple tools for validation and be mindful of potential performance impacts introduced by the tools themselves.

Tools are sometimes overrated. Any Tool should be chosen on the basis of merit in that particular use-case. In general, going for 2 tools may be a good strategy to validate results. Also, it should be noted that heavy tools themselves may impact the results.

In my perspective the tools are in the lowest place in importance. It doesn't matter if I have the best tool ever created if I measure something what is totally not relevant for my busines. Collect all needed prerequisites, then select the tool.

This is the most critical step as tools accuracy plays the main role. Before choose the correct tools, we must check or test all tools to get its accuracy then we can use it.

In my experience assessment of production system configurations depends on operational performances. No formal standards dictate approach or timing to measure IT performance. An array of powerful monitoring and reporting tools offers real-time insight into hardware and software performance. But these insights are intended to be tactical -- moment by moment, 24 hours a day. They show how each VM, server, disk and other resource operates -- and when those resources fail. Most Organization IT Production performance measurement processes depends on the following basic process to evaluate IT systems in productions. 1.Set the Goals 2.Define the Scope 3.Gather Data 4.Analyze and Access 5.Make recommendations

Selecting the appropriate methods is crucial for ensuring the accuracy and reliability of your system performance comparison. Methods encompass the procedures and techniques used to conduct the comparison and interpret the results. Your choice of methods should align with your specific system configuration, performance metrics, and analysis objectives. For instance, when evaluating the impact of different operating systems on security, methods like penetration testing, vulnerability scanning, or malware detection can be relevant. Comprehensive documentation that covers the reasons for tests, objectives, hypotheses, methods, and results, including system configurations, is essential for a thorough analysis.

Choosing the suitable strategies is a critical stage in guaranteeing the exactness and dependability of your framework execution examination. Strategies include the methods and procedures used to direct the correlation and decipher the outcomes. The selection of strategies ought to line up with your particular framework arrangement, execution measurements, and examination targets. For example, while evaluating what different working frameworks mean for security, strategies like entrance testing, weakness checking, or malware location might be suitable. In this manner, it's fundamental to pick techniques.

Methods and documentation. Documentation should cover all important elemets, like the reasons of tests, the goals, hypothesis, methods and results (with configuration of the system).

The most common pitfall with benchmarking performance is that many people trust the benchmarking results of 3rd party benchmarking tools as doctrine. Most benchmarks are artificial, synthetic workloads which do not accurately represent the intended workload on a given platform. They will always over-emphasize one aspect while de-emphasizing another which may be more appropriate for a given environment. A key point is that there are 2 ways to run benchmarks: validation benchmarks (which measure a specific workload), and more generalized synthetic benchmarks (eg. testing individual component performance). They are not interchangeable. The best way to avoid these pitfalls is to use real (or as close to real) production workloads to test.

One common pitfall in comparing system performance is overreliance on third-party benchmarking tools. These tools often use synthetic workloads that may not accurately represent your specific environment. It's important to distinguish between validation benchmarks that mimic real workloads and synthetic benchmarks that test individual components. Using real or production workloads that closely resemble your actual system usage can help mitigate this issue. Another pitfall is the use of inconsistent benchmark versions, which can lead to variations in results. To maintain consistency, ensure that you use the same version of benchmarking tools for all configurations. This practice helps in achieving fair and accurate performance comparisons.

Off the shelf benchmarking tools may not be useful all the times so at times you'll need custom solution for testing the system based on usage pattern. With cloud computing and various resources at disposal it is easy to mimic close to production workloads on various configurations. If you've a shared server where multiple software are running then you'll get all kinds of noise. Make sure to benchmark / record the resource utilization pattern on fresh systems. You can never be sure of the performance metrics if any setting changes with respect to the software.

Inconsistent Benchmark Versions: Using different versions of benchmarking software for separate tests, leading to variations in results. Ensure that you use the same version of benchmarking tools for all configurations to maintain consistency.

I completely echo what Benjamin said. Adapt the process to your needs. You are runing the tests against your objects, not someone's.

Learning from others who have conducted similar system performance comparisons is a valuable final step. It provides insights, tips, and best practices to enhance the quality and efficiency of your comparisons. However, it's essential to remember that what works in one environment may not apply to yours. Testing in your specific context is crucial, and there's no substitute for hands-on experience. While gathering information from others is helpful, validate it through testing to ensure it's relevant to your unique situation.

Gaining from other people who have led comparative or related framework execution correlations is an important last step. It gives an amazing chance to acquire bits of knowledge, significant hints, and best practices that can upgrade the quality and effectiveness of your own correlation endeavors. Contingent upon your particular situation, you can gain from others through different means like understanding articles, websites, or gathering conversations, watching recordings or webcasts, going to online classes, or drawing in with networks, gatherings, or organizations that share your advantage or mastery in framework execution examination.

Understand this, because someobody got a set of numbers in a given environment, thats probably not applicable to your environment.. TEST.. TEST.. you think you understand whats going on.. TEST.. you heard from a friend this is whats doing this TEST.. theres no substitute regardless of where you got your information from.. Remember any system can be tuned for a given benchmark, to give great numbers..

This step show us is the final result has the correct value or not.But, comparing the result with other contributions must have the environment and almost the same tools

After doing your homework and using bench marks tools available. I would say start with the minimum recommended specs with auto scaling horizontally enabled. Make use of stress testing, benchmark your hardware specs accordingly.

When it comes to hardware it honestly is a usr case basis. For instance I have worked in many companies where the use case for hardware will be dependant on the software and task required of the hardware. That being said... the best and most efficient way to compare hardware is to assess what you need it for. For instance if you game or use it for design, you need high end hardware that will not bottleneck under pressure, however if it is a standard user doing normal everyday tasks, you can use standard hardware which does not need to be tested against a use case.